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Staff MLOps Engineer

1 month, 2 weeks ago
United States
Full-time
Lead
Machine Learning Engineer
Software Development
AWS Azure C++ CI/CD Docker Embedded Systems GCP Git Go Kubernetes MLflow MLOps Python Reinforcement Learning Rust
Apply Now
Apptronik

Apptronik

Apptronik develops versatile humanoid robots to tackle tasks humans prefer not to do, aiming to reshape existence and enhance universal quality of life.

Aerospace & Defense
51-250
Founded 2015
$23M raised
View All Jobs 70

Description

  • Own the technical direction, architecture, and engineering standards for the MLOps platform.
  • Serve as the primary technical point of contact for Autonomy, Data Platform, and TeleOp on model lifecycle and platform contracts.
  • Design and operate the dataset lifecycle, including versioning, lineage, splits, and labeling handoff.
  • Ensure models can be traced back to the exact data and code used to produce them.
  • Build and operate the model registry, including artifacts, metadata, evaluation results, lineage, and approval workflows.
  • Define the promotion path from trained models to qualified models to deployed robot policies.
  • Create offline benchmarks, simulation rollouts, and policy-gating harnesses for model qualification.
  • Develop metrics and evaluation frameworks that the autonomy team can use to gate releases.
  • Own the packaging, versioning, rollback, and observability path from registered model to inference on Apollo robots.
  • Mentor engineers through code review, design review, and direct collaboration while influencing platform adoption across teams.

Requirements

  • Deep proficiency in Python and at least one systems-level language such as Go, Rust, or C++.
  • Proven experience owning and delivering an MLOps platform end-to-end in a production environment.
  • Experience with dataset versioning tools or equivalents such as DVC, LakeFS, or Delta.
  • Experience with experiment tracking tools such as MLflow, Weights & Biases, or Determined.
  • Strong background designing service-oriented systems on Kubernetes.
  • Experience defining evaluation and qualification frameworks for high-stakes ML systems such as robotics, safety-critical, or customer-facing products.
  • Experience leading technical projects end-to-end, including architecture, implementation, validation, and iteration.
  • Ability to lead by influence across teams and mentor other engineers.
  • Proficiency with cloud infrastructure such as AWS, GCP, or Azure, plus Docker, Git, and modern CI/CD workflows.
  • Master's degree in Computer Science, Machine Learning, or a related technical field preferred; Bachelor's degree considered with exceptional experience.
  • 8+ years of professional software engineering experience in ML platforms or related infrastructure, or 4+ years of direct hands-on experience owning an MLOps platform that shipped models to production.
  • Preferred: Experience deploying ML models to edge or embedded targets such as ONNX Runtime, TensorRT, or robot fleets.
  • Preferred: Experience with RL training and evaluation infrastructure for embodied agents.
  • Preferred: Familiarity with humanoid robotics, dexterous manipulation, or teleoperation data domains.
  • Preferred: Experience with simulation-in-the-loop evaluation using IsaacSim, MuJoCo, or equivalent.
  • Preferred: Familiarity with policy gating, shadow deployments, or staged rollout strategies for autonomy.
  • Preferred: Open-source contributions to MLOps tooling such as MLflow, BentoML, KServe, or Ray Serve.

Interested in this position?

Apply directly on the company website

Apply Now

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